Bringing Collaborations to Chemistry

I have been doing a lot of reading lately after a period last year when I did not touch a book. Mostly these books are related to science and published in the last year and I am devouring them at a rate of one per week or two. A recent one was very compelling and …

Its been a while since my last post here but its getting harder to find time between the 2 companies and writing grants. But one of the most interesting things I have been watching has been the roller coaster ride of the Pediatric Rare Disease priority review vouchers over a few years. The most recent …

Its been an incredibly fast paced year and hence my posting frequency has suffered. Perhaps its time to get back to writing . Today we had a press release with SRI so that triggered me to fill in the back story. Rewind back to 2014 and the Ebola outbreak was in full flow – see …

The frequency seems to be increasing where I see something that sets me off (head in hands saying why, why why?)..Over the past few years I have seen AI/ Deep Learning and drug discovery appearing often and that may not be a good thing. Now this could be the PR engines of the VC’s, companies …

If you have ever wondered how the pill you are taking for your disease originated, then nowadays you don’t have to go far to find out, as a quick search of Wikipedia will return the molecule, who makes it, and a wide array of prescribing information and warnings. This has not always been the case, …

Today I released the following Collaborations Pharmaceuticals Inc, announcement of our 3rd NIH grant this year. This is an SBIR and will fund software development to provide a tool the company will be able to use in its drug discovery programs but also make commercially available. It will build off our past publications on open source software. A very exciting step for the company which will fund more jobs!

Fuquay Varina – The National Institute of General Medical Sciences (NIGMS) recently awarded $149,999 to Collaborations Pharmaceuticals, Inc. (CPI) to develop software that could make public data more amenable to those scientists who want to use it to build computational models to help their research.

There are massive publically accessible databases that include a broad variety of disease targets and absorption, distribution, metabolism, excretion and toxicology (ADMET) properties that are not in a form that is immediately ready for machine learning model building or accessible for use by small research and development (R&D) organizations that do not have their own in-house cheminformatics teams. This project will compile a comprehensive collection of these datasets (e.g. databases like PubChem, ChEMBL etc) for structure-activity data. This will enable the user to quickly and automatically use machine learning models for various targets and properties that could be of value for drug discovery.

“Being able to use transparent computational models simultaneously for visualizing activity trends for multiple targets (both diseases and ADMET) removes the burden of curation or purchasing and maintaining expensive software, and drastically simplifies the addition of new data. It also represents a new frontier of drug discovery as a world of small, agile distributed R&D organizations has access to valuable public datasets that can inform their research. Such computational models will assist in drug repurposing efforts internally and with our collaborators while likely identifying new compounds for a wide array of drug discovery projects” said Sean Ekins, CEO CPI.

“We are very grateful to NIGMS for funding so we can illustrate how computational approaches can be used to repurpose drugs already approved for other uses and instead use for neglected and rare diseases” said Dr. Ekins.

About Collaborations Pharmaceuticals, Inc.

Collaborations Pharmaceuticals, Inc. performs research and development on innovative therapeutics for multiple rare and infectious diseases. We partner with leading academics, companies and foundations to identify and translate early preclinical to clinical stage assets. We have considerable experience in preclinical and computational approaches to drug discovery and toxicity prediction. For more information, please visit http://www.collaborationspharma.com/

A couple of weeks ago I was in Denver for the AAPS2016 meeting. This was the first time I had been at the meeting in quite a number of years and there were some changes. Firstly posters were now electronic and that did not seem a positive change for me. The loss of something to browse was tough. Having only 30 min windows to catch a presenter was also hard to do. I hope they go back to paper posters.

Also the conference center was like a giant cavern and seemed sparsely populated with attendees, apart from in the exhibit room which was pretty busy. The presentations I went to had handfuls of people in them, in generally huge rooms.

I was there to give a couple of presentations – one on drug repurposing in a session with perhaps 50 attendees and the other was on using social media for raising attention for papers and research etc. The latter was more educational presented along with the AAPS and was more interactive. The 20 or so attendees were new to most of the tools I described so I think a few people went away with a better idea of Kudos and Figshare etc.

It was a long way to go to give talks to relatively small audiences however it did help to raise awareness of the company and the postdoc job opening I have currently. Maybe it will be a few more years in between the next AAPS I attend.

I have worked from home for the better part of a decade either from my past location in PA or my present location in NC. Thanks to getting a recent grant, I now need to hire a postdoc and that means I can fly the nest (which in my case is my home). But with that comes a whole new array of challenges. This experience may be common to others who have spent long periods working from home who are in the same position (or not), so I thought I would share my experiences.

Working remotely for this long has many advantages – you can dress how you want, work when you want and pretty much do whatever you want – within reason if you are going to remain productive. The cons are just as long – well the main disadvantage is you feel like a prisoner working pretty much 24/7, living at home, especially if you have nothing nearby such as shops etc. Also the lack of interaction with others on a daily basis apart from by email or twitter makes you feel like you are totally isolated. For the past 5 yrs I have been a literal prisoner, I could have traveled to Mars and back in that time. Instead my only trips have been on business.. OK so you get the picture. So what happens if you have a small company (1-2) people, then what are your options? If you are in a big city you probably have lots of co-working space, or office space – you can rent a room/ cubicle as desired. But what if you want a lab, then your options plummet.

I live in what for many would be a great commuting location. 30min from Raleigh, 30 min from research triangle park, 50-60 min from Chapel Hill or Durham. And yes there are great offices and labs spaces in all these places ..but when it comes to a really small company again you are limited further because its not a spin out from UNC etc. My best bet so far is the incubator at NC State.. over 3 weeks ago I submitted an application (and today a response!) That and the 30 min commute made me want to think what could I do locally as well. I could also put that extra hour lost in commuting to good use. I also have emailed or visited several options locally and visited incubators in other states so I thought why not make a slide deck that might be useful to others undergoing the same hunt. Last week I put my incubator hunting experience up on slideshare.

This helped to get a bit of attention on twitter and folks told me about other places opening in the future. Today I met with three representatives from my local town and chamber of commerce. Literally this doubled the number of people I have met locally (beyond my optician, coffee shop /bakery, and picture frame shop). I clearly do not get out that much. So I gave them my pitch, what I do, what I would like to build (a biotech, here locally in Fuquay Varina), what I have done so far, how I want to create jobs here etc…I learnt the town would like to create an incubator, there is no time frame but first they need to find a building and measure the level of interest. So this may be my next crazy adventure, not only building a biotech but also a space for other start-ups. In the interim they provided some potential leads on small office space.. its a start. My bet is I will end up at the NC State incubator initially, and from there who knows.

At the weekend, while browsing an old building/ being used as an antiques warehouse in town I realized that it would be perfect for an incubator space- not too far from local amenities, centrally located, not too big, not too small…

What is missing from the press release and papers etc. is the back story. Why are we working on this and how did it start. Essentially it took a couple of years to get to this point, but it all started with tweets with Chris Southan and Megan Coffee discussing Ebola chemistry and screens. This lead to Peter Madrid and the work he had done previously on identifying a few antimalarials with activity, and then on from there, that provided a dataset for machine learning. When the models pulled up pyronaridine which we had also found for Chagas disease then I knew this was getting interesting. Once we had in vitro data to confirm the prediction we had enough for the paper and then to try to fund in vivo studies. This is were it gets expensive. I have reached out to the company in South Korea that makes pyronaridine for its antimalarial combination but so far no luck..

So in summary- a few tweets, followed by a lot of sweat equity from Peter, Rob, Joel and myself and then a ~$600K grant is obtained after 2 years. It does not always happen this way!

Well today’s post has been a few years in gestation and yes I have had to keep it quiet for a long, long time which was extended as our recent paper was embargoed for what seemed like an eternity. It starts back in 2009 when I was using some of the NIAID/SRI whole cell screening data for mycobacterium tuberculosis (here and here). This lead to building machine learning models to predict whole cell activity. One of the side effects of this were tables of fragments that were important or not in compounds used in the model. Little did I know that years later they would be used to design some new molecules and help find new antibiotics. I guess you could say this is an example of the unintended consequences of research. Over the years, predominantly with my collaborator Joel Freundlich (Rutgers) we have generated more machine learning models for TB (here and here). Then a few years back along with Gyanu Lamichhane at Johns Hopkins University we embarked on an R21 aimed at developing oral carbapenems for drug resistant TB. Joel wanted to make use of the data from the earlier machine learning models to design evolved carbapenems that would have properties that would bias them towards having activity against Mtb. Our new paper published today in Nature Chemical Biology is an extensive study on the mechanism of carbapenems which inhibit L,D-transpeptidases of Mtb as well as ESKAPE pathogens. The new evolved carbapenems made by Joel’s lab and tested by Gyanu’s lab, along with crystal structures, show how these compounds can bind covalently in different orientations in the enzyme binding site . This study also showed that biapenem was more potent against Mtb and was active in the mouse model (especially when combined with Rifampicin). This work represents an important step towards showing carbapenems are feasible for treating Mtb. It also highlights how some machine learning efforts can ultimately impact design of drugs, albeit not in the manner originally intended, but nonetheless creatively! It represents another collaboration that has been incredibly productive and goes some considerable way to showing what is possible when combining computational and experimental research for Mtb. I am eager to see where this work goes as Gyanu and Joel push this work onwards!

After several months our article in PLOS Neglected Tropical Diseases is now out from embargo and published. A big thank you to my co-authors Carolina Horta Andrade and Alexander Perryman who have put a lot of their time into the project. I would also like to thank the reviewers for their constructive comments. We have updated this article considerably since we put out the preprint in figshare back in August. As an added bonus we have made some slides which update the OpenZika story. Please tell others how they could contribute their computer time or Android phone (while charging) – every bit helps. We hope to update at some point with molecules we have tested that have been identified by OpenZika.

What started out as a challenge to model all the proteins in Zika and put it out into the open – turned into a “publishing” learning experience. Thankfully at least the information and the models were in repositories like figshare and F1000Research and not some other journal that would have kept the information closed until acceptance.

F1000Research had a seemingly impossible time finding reviewers – after we suggested what seemed like every possible person in their database and then kept coming back for more. Why was it so difficult to find reviewers for a manuscript on homology models – regardless of if its Zika or not? This is definitely puzzling.

After a month the cryo-EM and crystal structures started to appear in the PDB and in publications. In the six months this paper was not in the PDB we reckon several others have published one or more Zika homology models and had their papers in PubMed before ours – e.g. this and this …one even cited the F1000Research paper before it was “accepted” or in PubMed.

So my take away here is that – yes we got the information out there ASAP on models for Zika and we shared with the community everything, but F1000Research is not very visible on its own. PubMed is important to amplify manuscripts (alongside Google Scholar etc) – we lost >6 months of visibility even though we tried using Kudos to amplify and had 27 share referalls to date (see metrics in figure – as of today it has 2661 views, 707 downloads – so it appears these data are out of date) . Going forward we will be able to track the effect of being listed in PubMed. If only it could have been there when submitted to F1000Research.

Its likely that other scientists will have missed our work and gone off and done the same or similar. I think this could be prevented by making papers visible in PubMed while under review at Open “journals’ etc. This experience suggests that the open publishing, open review model is not there yet – there are still wrinkles to iron out IMHO.

Been in start up more fore over a year now I need to hire some help and get an office / lab. Ideally I am looking for a PhD or postdoc that wants to learn what I do for a couple of years. The posting below should describe the role. Feel free to connect (no recruiters) – I can only focus on those already in the USA that are legally able to work here. The job is based in the RTP area – office location to be determined.

Job Description

Position title: Postdoc for Drug Discovery

2 year – postdoctoral position is available working directly with CEO to further develop small molecules as treatments for various diseases. We are seeking a highly motivated, independent scientist with either chemistry or pharmacological expertise to join a new drug discovery company. The company is conducting highly interdisciplinary research focused on pre-clinical development of small molecules for neurological diseases, cancer, and infectious diseases. We collaborate widely with academic labs and are funded by multiple NIH grants. The successful candidate will be involved in coordinating external studies and building up our own laboratory capabilities in either pharmacology or chemistry. Must understand the drug discovery and development process and want to help translate treatments to the clinic. Their duties will include direct interaction with collaborators, analysis of data (ADME/PK, Pharmacology) and report/ paper/ grant writing.

Requirements

Applicant must have PhD in either pharmacology or medicinal chemistry or related topic and be a first author on at least 2-3 publications. This position requires extensive collaboration and problem solving experience. Chemistry and pharmaceutics expertise would be valuable. Excellent oral and written communication skills in English are required. Drug discovery experience in industry would be ideal.How to apply

Last tuesday and wednesday (Sept 13-14th) I was very honored to be an attendee at the “Progress Through Partnership: The NINDS 2016 Nonprofit Forum“. My role was to provide an industry perspective on several panels (alongside Dr. Ronald Marcus, Cerecor). There were a large number of rare disease (patients or parent) advocates, academic scientists and of course a large number of NINDS attendees. Going into this with no previous experience of the meeting I was a little unsure what I could add. But then as I sat there listening I think I had a unique perspective. I sit in an uneasy space as a scientist, a rare disease advocate and also someone that sees how small business can help. I am also impatient, so any hint that the science is not moving or that perhaps something is being missed, then I was on it. Apologies if it comes across as blunt but frankly with many diseases the progress has been too darn slow because of some key opinion leaders basically owning diseases and not having the ability to embrace new technologies or approaches that have been used elsewhere and could make the difference as outcome measures for clinical trials, for example. Below are some simple notes that I made – apologies if I missed anything while also on the panels on day 1, I also happened to live tweet which I have now storified.

Key points from Dr. Walter Koroshetz’s introduction – there were 161 registrants at this 10th non profit forum. He suggested we need to empower patients and power trials. He mentioned a vibrant translational program with Dr. Amir Tamiz. While the average number of grant applications increased by 25%, the average cost of grant is $379k. He mentioned one of the key policy issues being reproducibility. NINDS needs to support basic research (27% of budget). Some of the NINDS programs were mentioned including IGNITE, CREATE , BLUEPRINT as well as specific RFAs such as the Parkinson’s biomarkers program and the accelerated medicines partnership for Parkinson’s Disease.

Steve Kaminsky (International Rett Syndrome Foundation) their foundation saw 4 clinical sites would not be enough for recruiting and therefore built travel clinics (4-11 sites). He described the need for partnerships with NIH programs. Now other rare diseases are in the program, up to 15 sites using the infrastructure. The data created belongs to the nation, is protected, and the 15 sites have access. Pharma has to go through PI to get access, and the general public can interface with investigators who can study it (what if the patients wanted to study it – he did not seem to allow for this scenario?). The database was part of RDCRN. He mentioned it was too expensive to keep the database going. Over 7000 data fields for 1234 patients has resulted in publications of impact on sleep, effectiveness of drugs, diagnostics, anxiety etc. all from analysis of 12 yrs of data. He suggested the future is through the past and the past is natural history studies.

Michael Shy (University of Iowa) described Charcot-Marie-Tooth and the more than 90 genes identified. The INC RDCRN is in its 8th yr. They partner with advocacy groups. MDA, CMTA, and groups in the UK and Asia. They have 20 centers involved. He then focused on the CMT neuropathy score, described natural history studies as a science and how with CMT1A, measuring change that’s detectable is important He described frequent calls with researchers (I wondered are the calls with the RDCRN open to any advocacy groups or just close collaborator groups?).

Steve Roberds (Tuberous sclerosis alliance) owned their database. He discussed the difference between clinic entered vs patient entered data and getting input from scientists for data collection. Getting information from clinical data and electronic medical records was also described. There was some discussion around what happens after the end of a grant and who owns data, the long term sustainability of a database. Such databases present opportunities whether that is partnering with industry, (e.g. he described a collaborations with Novartis), who can fund changes to the database. They can also access without having to build the database. The alternative was Novartis Europe who collected their own data in Europe via a CRO, and learnt what they had so the elements matched with the US database. Steve also mentioned linking to biosamples and how with highly variable diseases e.g. Epilepsy, tumors, etc. this can add value to the database. He also described how custom built databases could be made simpler and facilitate analysis in order to get finding and papers out to in turn encourage utilization. You have to start a NHS sometime and it is a longtime commitment.

Petra Kauffman (NCATS) discussed disease registry vs NHS, and the need for forward looking data management coordination so that surveys can go out fast. She also mentioned that NCATS is launching a Toolkit for rare disease foundations as an Online portal.

Paul Gross (Hydrocephalus association) raised questions to be answered and discussed how to engage clinicians and partner with sites that lead in electronic medical/health records. Find clinicians on Epic’s (steering) board. These board clinicians advocate for inclusion of data sets.

Greg Farber (NIMH) described the national data archive at NIMH which houses data from 130,000 human subjects. Researchers can submit an application and summary data is available to anyone with a browser. Data on 800TB of image data. NINDS has FITBAR database. Holds data funded by other groups and uses common data elements in data dictionary. Every 6 months they request data from NIH grantees and validate data. They use a global unique identifier which is a hash code that can aggregate data on the same subject from multiple labs. Their database makes data potentially discoverable. Greg also mentioned the challenge of professional clinical research subjects and how to identify them and remove from clinical trials. The database is also citable and linked to the literature.

Shawn Murphy (Harvard Medical School) described how to get large sample size for trials by creating a large warehouse in the EHR. 6.7 million patients in such a database relates to 2.5 billion facts. Data in EHR is not very accurate, e.g. you can test for diagnosis using codes and notes in medical records. Finding patients by query construction in the database was also described. He mentioned that there were over 5000 registered users of tool. He had a NIH grant for i2b2 to create a community of developers, the Hive develops new tools. He also described using phenotyping algorithms to define cohorts of treatment resistant responsive. His goal was to create gold standard training sets. Such that the data could be used to build a classification algorithm to predict depression and improve detection. The CTSA at each site forms networks to combine the data and perform big data queries. For example, what is a normal child? Normal MRI provide visual guides and can be used to make new clinical decisions. He mentioned SHRINE and SMART software also.

Tamara Simon (University of Washington) described using databases to look at CSF shunt complications. She used the PHIS database used across 40 children’s hospitals. She mentioned the hydrocephalus core data project which provided a comprehensive, prospective study. This involved detailed data with 15 forms and 413 questions. This also enabled her to address what patient factors were associated with shunt failures, infection etc. PEDSNet database was also mentioned.

Panel 3. Strategies for biomarker identification

Shahshi Amur (FDA) described resources and tools, and different categories for biomarkers , risk, prognostic etc. biomarkers used in drug development and surrogate endpoints. Enablers for endpoints included data quality , assay imaging protocols. She also stated the difference between qualification and validation, To date 13 unique biomarkers have been qualified by the FDA, and 28 submissions. (She did not mention how long to process qualifications – and what it the cost). She did describe natural history data as useful for prognostic biomarkers using strength of evidence.

Other speakers in this session included Dr’s Katrina Gwinn (NINDS), Hao Wang (NINDS) and Petra Kaufmann (NCATS) who discussed agreeing on biomarker terminology and the need to make sure you are going after the right things for biomarkers. The heterogeneity of patients with disease was mentioned. Consortia were described for bringing groups together to look into them, how to stop reinvention. Standardizing and training staff to decrease noise in order to get the right treatment for the patient was also brought up. Standardization for bio specimens was thought important as well as the need to improve the quality of publications and the data that has to be associated with it. There was some mention of precompetitve efforts for biomarkers and the need overall for teamwork.

NIH101: priority setting, decision-making, NIH basics and discussion

Alan Willard (NINDS) presented on program considerations and the need to fund early stage investigators, concept clearance for proposed solicitations and influencing what they solicit through RFI. He also alerted us to how people could volunteer for peer review and go to the CSR website to volunteer expertise. There was also some mention of OnPAR as a way to alerting other foundations of applications that were not funded but may be useful for funding by others.

Panel 4. Developing Better Clinical Outcome measures

Ron Bartek (Friedreich’s Ataxia Research Alliance) moderated this session which included Michael Shy (Univ Iowa) who described outcome measures such as CMTNSv2, CMTPEDs (But did not describe how or if the scores are accepted by FDA). He also described the disability severity index, PCMT-QOL CMT health index and a recent paper that developed an MRI biomarker as published in Lancet neurology in 2016. Carsten Bonnemann (NINDS) again mentioned GAN and the importance of using outcome measures from other disorders and combining known outcome measures with exploratory measures. Ray Dorsey (Univ Rochester) suggested the need for novel outcome measures.
He illustrated this as we still use a 100 yr old plus method for scoring Parkinson’s disease. He proposed novel sensors, remote monitoring, wearables, implantables etc. as alternatives. Another example was how voice recording can detect Parkinson’s, Parkinsonism and related disorders from 2015. 15,000 people took part in mobile app based study. It is also possible to measure pharmacological improvement in an app. Huntingtons disease and step time was yet another example. He also mentioned the importance of measuring patients function in the home environment. He made an excellent comment at the end of his presentation about some thought leaders embracing technology (and yet there are many others that do not). Jacob Kean (University of Utah) discussed improving precision in patient reported outcomes, using and adaptive approach for using measures and short form selection. He also described how the NIH invested in PROMIS and the importance of tracking registry participants. Matthew Goodwin (Northeastern Univ) gave a very animated presentation which alerted the audience to the stickiness of technology and how wearables could give very clear kinematic signatures. Examples he used included tracking heart rate in autism, repeated measures which you can see groups clustering in. Also described were differences in hyper vs hypo aroused patients, and in epilepsy you can detect onset with wearable devices that can differentiate tonic and clonic signatures. There was also the potential to get devices to combine information with elements of citizen science for engagement. Some of the drawbacks include the cost of data analysis, in which case sampling might be good to find the signal. There is also the need for researchers that can do the modeling and the health sciences. Increasingly clinical companies are wanting to use mobile technologies.

To follow up my last post .. I can now announce some recent funding from NIAID for the TB work with Dr. Joel Freundlich ! Here is the PR –

Fuquay-Varina, North Carolina – The National Institute of Allergy and Infectious Diseases (NIAID) recently awarded $149,388 to Collaborations Pharma, Inc. (CPI) to initiate a partnership with Rutgers aimed at developing a series of compounds for treating tuberculosis (TB), an infectious disease generally affecting the lungs in humans and caused by the bacterium Mycobacterium tuberculosis (Mtb).

One-third of the global population is understood to be infected with TB and the disease continues to kill 1.5 million people every year and to infect approximately 9 million. Despite the availability of effective treatments for the disease, the combined impacts of drug resistance and morbidity of patients co-infected with HIV/AIDS have stimulated research on new quicker acting (less than the current six-month minimum) treatments efficacious against drug-resistant infections that are less toxic when used with anti-retroviral regimens for HIV/AIDS.

“We initially used Bayesian machine learning models to rediscover a class of compounds which seems to have been neglected for over 40 years ago. The compound we found has activity against drug-sensitive TB as well drug-resistant forms” said Sean Ekins, CEO CPI.

“To date my lab has made many analogs of the initial active compound we co-discovered with Dr. Ekins. Our plan is to focus on addressing limitations using computational models developed by CPI to see if we can arrive at a compound with good activity in an acute mouse model of the disease” said Dr. Joel S. Freundlich, Associate Professor of Pharmacology, Physiology & Neuroscience and Medicine at Rutgers University–New Jersey Medical School.

Dr. Freundlich has a chemical biology lab of eleven scientists that utilizes a multi-disciplinary approach to study infectious diseases, with a specific focus on tuberculosis. His lab will aim to identify potential drug candidates as well as the mechanism of action of this antitubercular class. Ultimately their goal is to optimize this compound class to develop a commercially viable new series of antibacterials.

“This work is a wonderful example of our efforts to involve outside companies in our search for novel antibacterials” said Dr. David Perlin, Executive Director, Professor at the Public Health Research Institute Center at Rutgers and Principal Investigator, NIH/NIAID Center of Excellence in Translational Research.

“We are very grateful to NIAID for funding this project as CPI is focused on collaborations with academia so that we can apply our computational approaches to real world applications that can impact research on neglected diseases” said Dr. Ekins.

About Rutgers New Jersey Medical School
Founded in 1954, Rutgers New Jersey Medical School is the oldest school of medicine in the state. Today it is part of Rutgers, The State University of New Jersey, and graduates approximately 170 physicians a year. Dedicated to excellence in education, research, clinical care and community outreach, the medical school comprises 22 academic departments and works with several healthcare partners, including its principal teaching hospital, Newark University Hospital. Its faculty consists of numerous world-renowned scientists and many of the region’s “top doctors.” New Jersey Medical School hosts more than 50 centers and institutes, including the Public Health Research Institute Center, the Global Tuberculosis Institute and the Neurological Institute of New Jersey. For more information please visit: njms.rutgers.edu

About Collaborations Pharmaceuticals, Inc.
Collaborations Pharmaceuticals, Inc. performs research and development on innovative therapeutics for multiple rare and infectious diseases. We partner with academics or companies to identify and translate early preclinical to clinical stage assets. We have considerable experience of preclinical and computational approaches to drug discovery and toxicity prediction. For more information, please visit http://www.collaborationspharma.com/